OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective
Venue: arXiv cs.NE 2024
Year: 2024

This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language Models (LLMs), our study investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes. Specifically, we highlight its effectiveness in illuminating key decision variables in evolutionarily optimized solutions while articulating contextual trade-offs. Tailored to address the challenges inherent in inferring complex multi-objective optimization solutions at scale, our approach emphasizes the adaptive nature of LLMs, allowing them to provide nuanced explanations and align their language with diverse stakeholder expertise levels and domain preferences. Empirical studies underscore the practical applicability and impact of LLM-Assisted Inference in real-world decision-making scenarios.
A Flow is a Stream of Packets: A Stream-Structured Data Approach for DDoS Detection
Venue: arXiv cs.CR 2024
Year: 2024

Distributed Denial of Service (DDoS) attacks are getting increasingly harmful to the Internet, showing no signs of slowing down. Developing an accurate detection mechanism to thwart DDoS attacks is still a big challenge due to the rich variety of these attacks and the emergence of new attack vectors. In this paper, we propose a new tree-based DDoS detection approach that operates on a flow as a stream structure, rather than the traditional fixed-size record structure containing aggregated flow statistics. Although aggregated flow records have gained popularity over the past decade, providing an effective means for flow-based intrusion detection by inspecting only a fraction of the total traffic volume, they are inherently constrained. Their detection precision is limited not only by the lack of packet payloads, but also by their structure, which is unable to model fine-grained inter-packet relations, such as packet order and temporal relations. Additionally, inferring aggregated flow statistics must wait for the complete flow to end. Here we show that considering flow inputs as variable-length streams composed of their associated packet headers, allows for very accurate and fast detection of malicious flows. We evaluate our proposed strategy on the CICDDoS2019 and CICIDS2017 datasets, which contain a comprehensive variety of DDoS attacks. Our approach matches or exceeds existing machine learning techniques' accuracy, including state-of-the-art deep learning methods. Furthermore, our method achieves significantly earlier detection, e.g., with CICDDoS2019 detection based on the first 2 packets, which corresponds to an average time-saving of 99.79% and uses only 4--6% of the traffic volume.
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics
Venue: arXiv cs.SE 2024
Year: 2024

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2-10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.
Information Rates Over Multi-View Channels
Venue: arXiv cs.IT 2024
Year: 2024

We investigate the fundamental limits of reliable communication over multi-view channels, in which the channel output is comprised of a large number of independent noisy views of a transmitted symbol. We consider first the setting of multi-view discrete memoryless channels and then extend our results to general multi-view channels (using multi-letter formulas). We argue that the channel capacity and dispersion of such multi-view channels converge exponentially fast in the number of views to the entropy and varentropy of the input distribution, respectively. We identify the exact rate of convergence as the smallest Chernoff information between two conditional distributions of the output, conditioned on unequal inputs. For the special case of the deletion channel, we compute upper bounds on this Chernoff information. Finally, we present a new channel model we term the Poisson approximation channel -- of possible independent interest -- whose capacity closely approximates the capacity of the multi-view binary symmetric channel for any fixed number of views.
An Approach for Decentralized Authentication in Networks of UAVs
Venue: arXiv cs.DC 2024
Year: 2024

We propose a decentralized authentication system for networks of unmanned aerial vehicles. A blockchain-based public key infrastructure allows the usage of public key cryptography and public key based authentication protocols. The blockchain provides a common storage of the public keys and their relations and can provide the required information for the authentication process. Furthermore, the unmanned aerial vehicles store selected parts of the blockchain in order to operate independently in areas where they might not have access to the Internet. This allows unmanned aerial vehicles to authenticate entities of the network, like other unmanned aerial vehicles, cloud services, cars, and any computer.
Architecture-Level Modeling of Photonic Deep Neural Network Accelerators
Venue: arXiv cs.ET 2024
Year: 2024

Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables low-energy, high-throughput optical-analog computations. To realize these benefits in a full system (accelerator + DRAM), designers must ensure that the benefits of using the electrical, optical, analog, and digital domains exceed the costs of converting data between domains. Designers must also consider system-level energy costs such as data fetch from DRAM. Converting data and accessing DRAM can consume significant energy, so to evaluate and explore the photonic system space, there is a need for a tool that can model these full-system considerations. In this work, we show that similarities between Compute-in-Memory (CiM) and photonics let us use CiM system modeling tools to accurately model photonics systems. Bringing modeling tools to photonics enables evaluation of photonic research in a full-system context, rapid design space exploration, co-design, and comparison between systems. Using our open-source model, we show that cross-domain conversion and DRAM can consume a significant portion of photonic system energy. We then demonstrate optimizations that reduce conversions and DRAM accesses to improve photonic system energy efficiency by up to 3x.
Distribution-Preserving Integrated Sensing and Communication with Secure Reconstruction
Venue: arXiv cs.IT 2024
Year: 2024

Distribution-preserving integrated sensing and communication with secure reconstruction is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. A numerical example demonstrates the tradeoff between the communication rate, reconstruction distortion and distribution preservation. Furthermore, we consider the case that the reconstructed sequence should be kept secret from an eavesdropper who also observes the channel output. An inner bound of the tradeoff region and a capacity-achieving special case are presented.
Zero Shot Context-Based Object Segmentation using SLIP (SAM+CLIP)
Venue: arXiv cs.CV 2024
Year: 2024

We present SLIP (SAM+CLIP), an enhanced architecture for zero-shot object segmentation. SLIP combines the Segment Anything Model (SAM) \cite{kirillov2023segment} with the Contrastive Language-Image Pretraining (CLIP) \cite{radford2021learning}. By incorporating text prompts into SAM using CLIP, SLIP enables object segmentation without prior training on specific classes or categories. We fine-tune CLIP on a Pokemon dataset, allowing it to learn meaningful image-text representations. SLIP demonstrates the ability to recognize and segment objects in images based on contextual information from text prompts, expanding the capabilities of SAM for versatile object segmentation. Our experiments demonstrate the effectiveness of the SLIP architecture in segmenting objects in images based on textual cues. The integration of CLIP's text-image understanding capabilities into SAM expands the capabilities of the original architecture and enables more versatile and context-aware object segmentation.
Point Resampling and Ray Transformation Aid to Editable NeRF Models
Venue: arXiv cs.CV 2024
Year: 2024

In NeRF-aided editing tasks, object movement presents difficulties in supervision generation due to the introduction of variability in object positions. Moreover, the removal operations of certain scene objects often lead to empty regions, presenting challenges for NeRF models in inpainting them effectively. We propose an implicit ray transformation strategy, allowing for direct manipulation of the 3D object's pose by operating on the neural-point in NeRF rays. To address the challenge of inpainting potential empty regions, we present a plug-and-play inpainting module, dubbed differentiable neural-point resampling (DNR), which interpolates those regions in 3D space at the original ray locations within the implicit space, thereby facilitating object removal & scene inpainting tasks. Importantly, employing DNR effectively narrows the gap between ground truth and predicted implicit features, potentially increasing the mutual information (MI) of the features across rays. Then, we leverage DNR and ray transformation to construct a point-based editable NeRF pipeline PR^2T-NeRF. Results primarily evaluated on 3D object removal & inpainting tasks indicate that our pipeline achieves state-of-the-art performance. In addition, our pipeline supports high-quality rendering visualization for diverse editing operations without necessitating extra supervision.
LayGA: Layered Gaussian Avatars for Animatable Clothing Transfer
Venue: arXiv cs.CV 2024
Year: 2024

Animatable clothing transfer, aiming at dressing and animating garments across characters, is a challenging problem. Most human avatar works entangle the representations of the human body and clothing together, which leads to difficulties for virtual try-on across identities. What's worse, the entangled representations usually fail to exactly track the sliding motion of garments. To overcome these limitations, we present Layered Gaussian Avatars (LayGA), a new representation that formulates body and clothing as two separate layers for photorealistic animatable clothing transfer from multi-view videos. Our representation is built upon the Gaussian map-based avatar for its excellent representation power of garment details. However, the Gaussian map produces unstructured 3D Gaussians distributed around the actual surface. The absence of a smooth explicit surface raises challenges in accurate garment tracking and collision handling between body and garments. Therefore, we propose two-stage training involving single-layer reconstruction and multi-layer fitting. In the single-layer reconstruction stage, we propose a series of geometric constraints to reconstruct smooth surfaces and simultaneously obtain the segmentation between body and clothing. Next, in the multi-layer fitting stage, we train two separate models to represent body and clothing and utilize the reconstructed clothing geometries as 3D supervision for more accurate garment tracking. Furthermore, we propose geometry and rendering layers for both high-quality geometric reconstruction and high-fidelity rendering. Overall, the proposed LayGA realizes photorealistic animations and virtual try-on, and outperforms other baseline methods. Our project page is https://jsnln.github.io/layga/index.html.
Power Evaluation of IOT Application Layer Protocols
Venue: arXiv cs.NI 2024
Year: 2024

The Internet of Things has affected all aspects of daily life, and the number of IoT devices is increasing day by day. According to forecasts, the number of Internet of Things devices will reach one trillion devices by 2035. The increase in the number of devices connected to the Internet will cause various concerns. One of the most important concerns is the energy and power consumption of these devices. Although Internet of Things modules are low in energy consumption, their widespread and large-scale use has made the issue of power consumption become the most important challenge in this field. For this reason, it is necessary to use communication protocols that, in addition to establishing efficient communication, impose minimal power consumption on the network. In this paper, application layer protocols such as MQTT, MQTT-SN, CoAP, and HTTP are simulated using the tools available in the Contiki operating system, including COOJA and Powertrace, and they { are evaluated} and compared with each other in terms of power consumption. According to the simulations performed by the mentioned tools, the MQTT-SN protocol was the least consuming protocol in terms of power consumption. After that, the CoAP protocol is placed, and with a slight difference, the MQTT protocol, which consumes more than MQTT-SN. Finally, the HTTP protocol consumes the most power, which makes it unsuitable for communication in the Internet of Things
Tremor Reduction for Accessible Ray Based Interaction in VR Applications
Venue: arXiv cs.HC 2024
Year: 2024

Comparative to conventional 2D interaction methods, virtual reality (VR) demonstrates an opportunity for unique interface and interaction design decisions. Currently, this poses a challenge when developing an accessible VR experience as existing interaction techniques may not be usable by all users. It was discovered that many traditional 2D interface interaction methods have been directly converted to work in a VR space with little alteration to the input mechanism, such as the use of a laser pointer designed to that of a traditional cursor. It is recognized that distanceindependent millimetres can support designers in developing interfaces that scale in virtual worlds. Relevantly, Fitts law states that as distance increases, user movements are increasingly slower and performed less accurately. In this paper we propose the use of a low pass filter, to normalize user input noise, alleviating fine motor requirements during ray-based interaction. A development study was conducted to understand the feasibility of implementing such a filter and explore its effects on end users experience. It demonstrates how an algorithm can provide an opportunity for a more accurate and consequently less frustrating experience by filtering and reducing involuntary hand tremors. Further discussion on existing VR design philosophies is also conducted, analysing evidence that supports multisensory feedback and psychological models. The completed study can be downloaded from GitHub.
Data Trading Combination Auction Mechanism based on the Exponential Mechanism
Venue: arXiv cs.GT 2024
Year: 2024

With the widespread application of machine learning technology in recent years, the demand for training data has increased significantly, leading to the emergence of research areas such as data trading. The work in this field is still in the developmental stage. Different buyers have varying degrees of demand for various types of data, and auctions play a role in such scenarios due to their authenticity and fairness. Recent related work has proposed combination auction mechanisms for different domains. However, such mechanisms have not addressed the privacy concerns of buyers. In this paper, we design a \textit{Data Trading Combination Auction Mechanism based on the exponential mechanism} (DCAE) to protect buyers' bidding privacy from being leaked. We apply the exponential mechanism to select the final settlement price for the auction and generate a probability distribution based on the relationship between the price and the revenue. In the experimental aspect, we consider the selection of different mechanisms under two scenarios, and the experimental results show that this method can ensure high auction revenue and protect buyers' privacy from being violated.
Distributed Lovász Local Lemma under Bandwidth Limitations
Venue: arXiv cs.DS 2024
Year: 2024

The constructive Lovász Local Lemma has become a central tool for designing efficient distributed algorithms. While it has been extensively studied in the classic LOCAL model that uses unlimited bandwidth, much less is known in the bandwidth-restricted CONGEST model. In this paper, we present bandwidth- and time-efficient algorithms for various subclasses of LLL problems, including a large class of subgraph sampling problems that are naturally formulated as LLLs. Lastly, we use our LLLs to design efficient CONGEST algorithms for coloring sparse and triangle-free graphs with few colors. These coloring algorithms are exponentially faster than previous LOCAL model algorithms.
CaFA: Global Weather Forecasting with Factorized Attention on Sphere
Venue: arXiv cs.LG 2024
Year: 2024

Accurate weather forecasting is crucial in various sectors, impacting decision-making processes and societal events. Data-driven approaches based on machine learning models have recently emerged as a promising alternative to numerical weather prediction models given their potential to capture physics of different scales from historical data and the significantly lower computational cost during the prediction stage. Renowned for its state-of-the-art performance across diverse domains, the Transformer model has also gained popularity in machine learning weather prediction. Yet applying Transformer architectures to weather forecasting, particularly on a global scale is computationally challenging due to the quadratic complexity of attention and the quadratic increase in spatial points as resolution increases. In this work, we propose a factorized-attention-based model tailored for spherical geometries to mitigate this issue. More specifically, it utilizes multi-dimensional factorized kernels that convolve over different axes where the computational complexity of the kernel is only quadratic to the axial resolution instead of overall resolution. The deterministic forecasting accuracy of the proposed model on $1.5^\circ$ and 0-7 days' lead time is on par with state-of-the-art purely data-driven machine learning weather prediction models. We also showcase the proposed model holds great potential to push forward the Pareto front of accuracy-efficiency for Transformer weather models, where it can achieve better accuracy with less computational cost compared to Transformer based models with standard attention.
Exploring the Effects of User-Agent and User-Designer Similarity in Virtual Human Design to Promote Mental Health Intentions for College Students
Venue: arXiv cs.HC 2024
Year: 2024

Virtual humans (i.e., embodied conversational agents) have the potential to support college students' mental health, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields where students are at a heightened risk of mental disorders such as anxiety and depression. A comprehensive understanding of students, considering their cultural characteristics, experiences, and expectations, is crucial for creating timely and effective virtual human interventions. To this end, we conducted a user study with 481 computer science students from a major university in North America, exploring how they co-designed virtual humans to support mental health conversations for students similar to them. Our findings suggest that computer science students who engage in co-design processes of virtual humans tend to create agents that closely resemble them demographically--agent-designer demographic similarity. Key factors influencing virtual human design included age, gender, ethnicity, and the matching between appearance and voice. We also observed that the demographic characteristics of virtual human designers, especially ethnicity and gender, tend to be associated with those of the virtual humans they designed. Finally, we provide insights concerning the impact of user-designer demographic similarity in virtual humans' effectiveness in promoting mental health conversations when designers' characteristics are shared explicitly or implicitly. Understanding how virtual humans' characteristics serve users' experiences in mental wellness conversations and the similarity-attraction effects between agents, users, and designers may help tailor virtual humans' design to enhance their acceptance and increase their counseling effectiveness.
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle
Venue: arXiv cs.LG 2024
Year: 2024

In Reinforcement Learning (RL), artificial agents are trained to maximize numerical rewards by performing tasks. Exploration is essential in RL because agents must discover information before exploiting it. Two rewards encouraging efficient exploration are the entropy of action policy and curiosity for information gain. Entropy is well-established in literature, promoting randomized action selection. Curiosity is defined in a broad variety of ways in literature, promoting discovery of novel experiences. One example, prediction error curiosity, rewards agents for discovering observations they cannot accurately predict. However, such agents may be distracted by unpredictable observational noises known as curiosity traps. Based on the Free Energy Principle (FEP), this paper proposes hidden state curiosity, which rewards agents by the KL divergence between the predictive prior and posterior probabilities of latent variables. We trained six types of agents to navigate mazes: baseline agents without rewards for entropy or curiosity, and agents rewarded for entropy and/or either prediction error curiosity or hidden state curiosity. We find entropy and curiosity result in efficient exploration, especially both employed together. Notably, agents with hidden state curiosity demonstrate resilience against curiosity traps, which hinder agents with prediction error curiosity. This suggests implementing the FEP may enhance the robustness and generalization of RL models, potentially aligning the learning processes of artificial and biological agents.